SCIEN Talk

In this presentation, I will talk about some of the recent work we did on new methods for reconstructing computer graphics models of real world scenes from sparse or even monocular video data. These methods are based of bringing together neural network-based and explicit model-based approaches. I will also talk about new neural rendering approaches that combine explicit model-based and neural network based concepts for image formation in new ways. They enable new means to synthesize highly realistic imagery and videos of real work scenes under user control.

Inverse problems involving light abound throughout many scientific disciplines. Typically, a set of images captured by an instrument must be mathematically processed to reveal some property of our physical reality. This talk will provide an introduction and overview of the emerging field of differentiable physically based rendering, which has the potential of substantially improving the accuracy of such calculations.
Methods in this area propagate derivative information through physical light simulations to solve optimization problems. While still very much a work in progress, advances in the last years have led to increasingly efficient and numerically robust methods that can begin to tackle interesting real-world problems. I will give an overview of recent progress and open problems.

Freeform optics has set a new path for optical system design across a wide range of applications spanning from microscopy to space optics, including the billion $ consumer market of near-eye displays for augmented reality that set us on this technology path in the first place. Today, freeform optics have been demonstrated to yield compact, achromatic, and high-performance imaging systems that are poised to enable the science of tomorrow. This talk will introduce freeform optics and highlight emerging design methods. We will then present success stories in digital-viewfinder, imager, and spectrometer designs, which we anticipate will ignite discussion and stimulate cooperation in enabling knowledge in freeform optics. Building on this foundation, we will introduce the concept of a metaform to address a need in near-eye displays.

The design of material appearance for both virtual and physical design remains a challenging problem. There aren’t straightforward intuitive techniques as there are in  geometric design where shapes can be sketched or assembled from geometric primitives. In this talk I will present a series of contributions to developing intuitive appearance design tools. This includes studies of material appearance perception which form the basis of the development of perceptual axes for reflectance distribution design. I will also present novel interfaces for design including hybrid slider/image navigation and augmented reality interfaces. I will discuss the unique problems involved in designing appearance for objects to be physically manufactured rather than simply displayed in virtual environments.  Finally, I will show how exemplars of spatially varying materials can be inverted to produce procedural models.

In this talk I will discuss the phasor approach technique in fluorescence lifetime imaging microscopy (FLIM) as a novel method to measure metabolic alteration as a function of extracellular matrix (ECM) mechanics. To measure mitochondria contribution to metabolic switching, we developed a new algorithm called "mitometer" that is unbiased and allows for automated segmentation and tracking of mitochondria in live cell 2D and 3D time-lapse images. I will show how Mitometer measures mitochondria of triple-negative breast cancer cells. Results show they are faster, more directional, and more elongated than those in their receptor-positive counterparts. Furthermore, Mitometer shows that mitochondrial motility and morphology in breast cancer, but not in normal breast epithelia, correlate with fractions of the reduced form of NADH. Together, the automated segmentation and tracking algorithms and the innate user interface make Mitometer a broadly accessible tool.

Is computational imaging the next frontier in computer vision? As materials, lighting, and geometry become more complex an ordinary camera starts to be the limiting step in the vision pipeline. A case in point are transparent objects which cannot be seen by an ordinary camera (which mimics the human eye). However, a plenoptic camera that captures polarization spawns a unique texture to previously invisible objects. The polarized texture results from complex light-matter interactions in shape and refractive index. Such texture can be subsequently modeled and “baked” into neural pipelines to enable inference on transparent objects, a decades-open task in traditional computer vision. WIth polarization simply scratching the surface of what computational imaging can do, we end with a discussion of how broader forms of plenoptic data can be leveraged in future neural pipelines.

Recent advances in human vision research have pointed toward a theory that unifies many aspects of vision that are relevant to applications. According to this theory, loss of information in peripheral vision determines performance on many visual tasks. This theory subsumes old concepts such as visual saliency, selective attention, and change blindness. It predicts the rich details we have access to at a glance. Furthermore, it provides insight into tasks not commonly studied in human vision, such as the ability to comprehend connections in a graph, or to compare information across space.

In this talk, Lee will go through 3 parts:

1. His entrepreneurship journey and the formation of Blue River Technology from a class at Stanford to an idea worth working on, to a company operating on 10% of the lettuce in the US.
2. The computer vision technology and journey behind the see and spray system up until acquisition
3. Advances in control systems

Stanford Medical Mixed Reality (SMMR) invites you to their next virtual panel discussion series about groundbreaking mixed reality technology and innovation in medicine and how it impacts patients, clinicians, and the healthcare industry.

The event will start with a one-hour panel discussion featuring Prof. Allison Okamura, director of Stanford's Collaborative Haptics and Robotics in Medicine (CHARM) Lab; Dr. Danny Goel of PrecisionOS, a Vancouver-based company developing a VR surgical training platform; Craig Douglass of Contact CI, a Cincinnati-based company developing a haptic glove for immersive technologies; and Govinda Payyavula ofIntuitive Surgical, a Bay Area-based company and leader in robotic surgery systems. This panel will be moderated by Dr. Joel Sadler, an XR founder and technologist, and Dr. Christoph Leuze of the Stanford Medical Mixed Reality (SMMR) program. Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.

Imaging is the basic building block for automotive autonomous driving. Any computer vision system will require a good image as an input at all driving conditions. GatedVision provides an extra layer on top of the regular RGB/RCCB sensor to augment it at night time and harsh weather conditions. GatedVision images in darkness and different weather conditions will be shared. Imagine that you could detect a small target laying on the road having the same reflectivity as the back ground meaning no contrast, GatedVision can manipulate the way an image is captured so that contrast can be extracted. Additional imaging capabilities of GatedVision will be presented.